1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
#!/usr/bin/env python3
from __future__ import annotations
import json
import sqlite3
from pathlib import Path
from typing import Any
import numpy as np
from sentence_transformers import SentenceTransformer
import os
from pathlib import Path
DB_PATH = Path("cheat.db")
CARDS_PATH = Path("./cards.jsonl")
MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
LOCAL_CACHE_DIR = Path("models/hf")
LOCAL_CACHE_DIR = Path("models/hf")
os.environ.setdefault("HF_HOME", str(LOCAL_CACHE_DIR.resolve()))
os.environ.setdefault("SENTENCE_TRANSFORMERS_HOME", str(LOCAL_CACHE_DIR.resolve()))
REQUIRED_FIELDS = {
"id",
"intent",
"command",
"alternatives",
"explanation",
"requires",
"packages",
"tags",
"platform",
"shell",
"safety",
}
def load_cards(path: Path) -> list[dict[str, Any]]:
cards: list[dict[str, Any]] = []
with path.open("r", encoding="utf-8") as f:
for line_no, line in enumerate(f, start=1):
line = line.strip()
if not line:
continue
try:
card = json.loads(line)
except json.JSONDecodeError as e:
raise ValueError(f"Invalid JSON on line {line_no}: {e}") from e
missing = REQUIRED_FIELDS - set(card.keys())
if missing:
raise ValueError(f"Missing required fields on line {line_no}: {sorted(missing)}")
cards.append(card)
return cards
def build_search_text(card: dict[str, Any]) -> str:
"""
Build a compact semantic representation for embedding.
This is what the retriever will search over.
"""
parts: list[str] = []
intents = card.get("intent", [])
tags = card.get("tags", [])
command = card.get("command", "")
explanation = card.get("explanation", "")
alternatives = card.get("alternatives", [])
requires = card.get("requires", [])
platform = card.get("platform", [])
if intents:
parts.append("Intents: " + " | ".join(intents))
if tags:
parts.append("Tags: " + ", ".join(tags))
if command:
parts.append("Command: " + command)
if alternatives:
parts.append("Alternatives: " + " | ".join(alternatives))
if explanation:
parts.append("Explanation: " + explanation)
if requires:
parts.append("Requires: " + ", ".join(requires))
if platform:
parts.append("Platform: " + ", ".join(platform))
return "\n".join(parts)
def serialize_embedding(vec: np.ndarray) -> bytes:
return vec.astype(np.float32).tobytes()
def upsert_card(conn: sqlite3.Connection, card: dict[str, Any], search_text: str) -> None:
conn.execute("""
INSERT INTO cards (
id, command, explanation, intent_json, alternatives_json, requires_json,
packages_json, tags_json, platform_json, shell_json, safety, search_text, updated_at
)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
ON CONFLICT(id) DO UPDATE SET
command=excluded.command,
explanation=excluded.explanation,
intent_json=excluded.intent_json,
alternatives_json=excluded.alternatives_json,
requires_json=excluded.requires_json,
packages_json=excluded.packages_json,
tags_json=excluded.tags_json,
platform_json=excluded.platform_json,
shell_json=excluded.shell_json,
safety=excluded.safety,
search_text=excluded.search_text,
updated_at=CURRENT_TIMESTAMP
""", (
card["id"],
card["command"],
card["explanation"],
json.dumps(card["intent"], ensure_ascii=False),
json.dumps(card["alternatives"], ensure_ascii=False),
json.dumps(card["requires"], ensure_ascii=False),
json.dumps(card["packages"], ensure_ascii=False),
json.dumps(card["tags"], ensure_ascii=False),
json.dumps(card["platform"], ensure_ascii=False),
json.dumps(card["shell"], ensure_ascii=False),
card["safety"],
search_text,
))
def upsert_embedding(
conn: sqlite3.Connection,
card_id: str,
model_name: str,
vec: np.ndarray,
) -> None:
conn.execute("""
INSERT INTO card_embeddings (
card_id, model_name, embedding_blob, embedding_dim
)
VALUES (?, ?, ?, ?)
ON CONFLICT(card_id) DO UPDATE SET
model_name=excluded.model_name,
embedding_blob=excluded.embedding_blob,
embedding_dim=excluded.embedding_dim
""", (
card_id,
model_name,
serialize_embedding(vec),
int(vec.shape[0]),
))
def main() -> None:
if not DB_PATH.exists():
raise FileNotFoundError(
f"Database not found at {DB_PATH}. Run scripts/init_db.py first."
)
if not CARDS_PATH.exists():
raise FileNotFoundError(f"Cards file not found at {CARDS_PATH}")
cards = load_cards(CARDS_PATH)
model = SentenceTransformer(
MODEL_NAME,
cache_folder=str(LOCAL_CACHE_DIR.resolve()),
local_files_only=True,
)
search_texts = [build_search_text(card) for card in cards]
embeddings = model.encode(
search_texts,
normalize_embeddings=True,
convert_to_numpy=True,
show_progress_bar=True,
)
conn = sqlite3.connect(DB_PATH)
try:
conn.execute("PRAGMA foreign_keys=ON;")
for card, vec, search_text in zip(cards, embeddings, search_texts):
upsert_card(conn, card, search_text)
upsert_embedding(conn, card["id"], MODEL_NAME, vec)
conn.commit()
print(f"Indexed {len(cards)} cards into {DB_PATH}")
finally:
conn.close()
if __name__ == "__main__":
main()
|
